{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  一、案例简介\n",
    "## 1. 案例背景\n",
    ">   随着时代的进步，航空航天作为我国的战略性发展事业，取得了极大成就。航天事业的发展核心是飞行安全，飞行安全既是飞行人员与乘客的生命安全保障，又是航天科技发展的方向和目标。气象条件对飞行的影响是不同的，也是不可避免的，航天部门应对飞行威胁较大的恶劣天气进行分析，采取相应的对策进行防范，不断提高飞行的安全性与可靠性。\n",
    ">   恶劣天气的类型很多，主要包括大风、云、雷电、暴雨、冰雪，另外，大雾、风切变、冰高空急流等也会对飞行安全产生不同程度的影响。恶劣天气的影响具有不确定性，因而应减少在恶劣天气范围内进行飞行。\n",
    ">   通过天气因素上座数量的分析，预测不同天气情况下航班的上座情况，进而调整飞行计划以应对不同天气带来的影响。\n",
    "\n",
    "## 2. 案例意义\n",
    ">   从分析结果将使航空公司依据天气模式调整飞行时间表。它也可以引导乘客做出新的选择。航空公司可以提前几天预测到未来航班上座情况，然后未雨绸缪地进行调整。航空公司也可以预先发出警告更有效分配自己的资源、地勤人员、飞行人员和其他资产以减少损失\n",
    "## 3. 业务图\n",
    "![业务图](image-001.png)\n",
    "## 4. 案例目标\n",
    ">     现在，大多数航班只能对连续进行重复，一般处理天气延误的航班是在其发生之后。我们的大数据能够让他们预先预测延迟，以更好地与乘客沟通，以及优化资源。通过数据分析，预测假设航班和不同天气情况之间的结果，帮助迅速提前发现因天气发生的上座影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、相关技术\n",
    "- ## 数据采集，通过pymysql连接mysql数据库获取数据 \n",
    "- ## 数据预处理，通过pandas、sklearn进行数据清洗，转换等预处理\n",
    "- ## 数据探索，通过matplotlib及pyecharts进行数据可视化，发现数据规律\n",
    "- ## 数据分析建模，通过机器学习算法库sklearn学习已有数据规律建模，预测指定航班在不同天气下的上座率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、案例步骤"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 1. 数据采集: 从数据库casepro中获取数据表t_plane_order\\t_plane_weather,将表格转存在本地csv并展示数据\n",
    "\n",
    "> 数据库信息： host：10.102.52.248，port=3306， user=\"root\", passwd=\"root\"\n",
    "\n",
    "> 航班订单数据，包含航班时刻表，航班号，子订单，飞行日期，头等舱，公务舱，经济舱，其他，头等舱总数，公务舱总数，经济舱总数，其他总数。\n",
    "\n",
    "> 天气数据，包含基线：日期，高温，低温，天气状况，风，空气"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymysql #导入模块\n",
    "import pandas as pd\n",
    "\n",
    "#远程连接数据库\n",
    "db = pymysql.connect(\n",
    "         host='10.102.52.248',\n",
    "         port=3306,\n",
    "         user='root',\n",
    "         passwd='root',\n",
    "         db='casepro',\n",
    "         charset='utf8'\n",
    "         )\n",
    "def link_mysql(db):\n",
    "    #使用cursor()方法获取操作游标 \n",
    "    cursor = db.cursor()\n",
    "    sql = \"\"\"SELECT * FROM `t_plane_order`\"\"\"\n",
    "    sql1 = \"\"\"SELECT * FROM `t_plane_weather`\"\"\"\n",
    "    try:\n",
    "        cursor.execute(sql)  # 执行sql语句\n",
    "        result = cursor.fetchall()\n",
    "        columns = ['航班时刻表','航班号','子订单','日期','头等舱','公务舱','经济舱','其他','头等舱总数','公务舱总数','经济舱总数','其他总数']\n",
    "        frame = pd.DataFrame(list(result),columns=columns)\n",
    "        frame.to_csv(\"航班天气因素上座情况预测分析案例.csv\",encoding='utf-8')\n",
    "        cursor.execute(sql1)  # 执行sql语句\n",
    "        result1 = cursor.fetchall()\n",
    "        columns_weather = ['日期','高温','低温','天气状况','风','空气']\n",
    "        frame_weather = pd.DataFrame(list(result1),columns=columns_weather)\n",
    "        frame_weather.to_csv(\"天气数据.csv\",encoding='utf-8')\n",
    "    except Exception:\n",
    "        db.rollback()  # 发生错误时回滚\n",
    "        print(\"查询失败\")\n",
    "    cursor.close()  # 关闭游标\n",
    "    db.close()  # 关闭数据库连接\n",
    "link_mysql(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 2、数据预处理（清洗、数值化转换、标准化等）\n",
    "\n",
    "> 空数据判断和处理（注意分析空占比，区分数值型和类别型）\n",
    "\n",
    "> 数据规范性检查（格式、范围等）\n",
    "\n",
    "> 去除不规范数据、无效、无分析价值数据等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#对航班数据进行清理\n",
    "def yucli_plane():\n",
    "    df_plane = pd.read_csv('航班天气因素上座情况预测分析案例.csv',encoding='utf-8')\n",
    "    df_plane = pd.DataFrame(data=df_plane)\n",
    "    df_plane = df_plane.drop(columns='Unnamed: 0')\n",
    "    #判断其空占比\n",
    "    df_plane_percent = df_plane.isna().sum().sort_values(ascending=False) / len(df_plane)\n",
    "    print(\"各字段的空占比为：\\n{}\".format(df_plane_percent))\n",
    "    #删除空数据\n",
    "    df_plane_notNu = df_plane.dropna(axis=0)\n",
    "    #删除重复数据\n",
    "    df_plane_notNu = df_plane_notNu.drop_duplicates()\n",
    "    #将str类型改为float\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i] = df_plane_notNu.iloc[:,i].astype(float)\n",
    "    #将负值替换成零\n",
    "    for i in range(4,12):\n",
    "        df_plane_notNu.iloc[:,i][df_plane_notNu.iloc[:,i] < 0] = 0.0\n",
    "    #删除日期中不规范的数据\n",
    "    df_plane_notNu[df_plane_notNu.iloc[:,3] == \"0.0\"] = np.nan\n",
    "    df_plane_notNu.dropna(axis=0)\n",
    "    return df_plane_notNu\n",
    "\n",
    "#对天气数据进行处理\n",
    "def yucli_weath():\n",
    "    df_weath = pd.read_csv('天气数据.csv',encoding='utf-8')\n",
    "    df_weath = pd.DataFrame(df_weath)\n",
    "    #删除无分析价值数据\n",
    "    df_weath = df_weath.drop('Unnamed: 0',axis=1)\n",
    "    #将日期和空气格式化\n",
    "    df_weath['日期'] = df_weath['日期'].apply(lambda x:str(x)[0:10])\n",
    "    df_weath['空气'] = df_weath['空气'].apply(lambda x:str(x)[:-1])\n",
    "    return df_weath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3249: DtypeWarning: Columns (3,5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  if (await self.run_code(code, result,  async_=asy)):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "各字段的空占比为：\n",
      "子订单      0.272959\n",
      "航班号      0.013529\n",
      "日期       0.000389\n",
      "其他总数     0.000000\n",
      "经济舱总数    0.000000\n",
      "公务舱总数    0.000000\n",
      "头等舱总数    0.000000\n",
      "其他       0.000000\n",
      "经济舱      0.000000\n",
      "公务舱      0.000000\n",
      "头等舱      0.000000\n",
      "航班时刻表    0.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\python\\lib\\site-packages\\ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
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       "      <th></th>\n",
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       "      <th>航班号</th>\n",
       "      <th>天气状况</th>\n",
       "      <th>风</th>\n",
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       "      <td>重度污染</td>\n",
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       "      <td>南风1-2级</td>\n",
       "      <td>重度污染</td>\n",
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      "text/plain": [
       "                                         航班时刻表  头等舱  公务舱   经济舱   其他  头等舱总数  \\\n",
       "日期         航班号    天气状况 风       空气                                            \n",
       "2016-01-02 8160   霾~雾  无持续风向微风 严重污染  1615205.0  0.0  0.0   0.0  0.0   16.0   \n",
       "           9581   霾~雾  无持续风向微风 严重污染  2297904.0  0.0  0.0  15.0  0.0   24.0   \n",
       "           BR715  霾~雾  无持续风向微风 严重污染  4525245.0  0.0  0.0   8.0  3.0    0.0   \n",
       "           CA101  霾~雾  无持续风向微风 严重污染   400218.0  0.0  0.0   0.0  0.0   48.0   \n",
       "           CA107  霾~雾  无持续风向微风 严重污染   404502.0  0.0  0.0   2.0  0.0   32.0   \n",
       "...                                        ...  ...  ...   ...  ...    ...   \n",
       "2016-12-31 ZH9157 霾    南风1-2级  重度污染  3144374.0  0.0  0.0  15.0  0.0   24.0   \n",
       "           ZH9159 霾    南风1-2级  重度污染  3949220.0  0.0  0.0   0.0  0.0   24.0   \n",
       "           ZH9163 霾    南风1-2级  重度污染  3068798.0  3.0  0.0  45.0  0.0   24.0   \n",
       "           ZH9165 霾    南风1-2级  重度污染  3072106.0  0.0  0.0  10.0  0.0   24.0   \n",
       "           ZH9168 霾    南风1-2级  重度污染  3083458.0  0.0  0.0  15.0  0.0    0.0   \n",
       "\n",
       "                                     公务舱总数   经济舱总数   其他总数    高温    低温  \n",
       "日期         航班号    天气状况 风       空气                                      \n",
       "2016-01-02 8160   霾~雾  无持续风向微风 严重污染    0.0   324.0    0.0  10.0  -8.0  \n",
       "           9581   霾~雾  无持续风向微风 严重污染    0.0   450.0    0.0  15.0 -12.0  \n",
       "           BR715  霾~雾  无持续风向微风 严重污染  195.0  1190.0  280.0  25.0 -20.0  \n",
       "           CA101  霾~雾  无持续风向微风 严重污染    0.0   483.0    0.0  15.0 -12.0  \n",
       "           CA107  霾~雾  无持续风向微风 严重污染    0.0   322.0    0.0  15.0 -12.0  \n",
       "...                                    ...     ...    ...   ...   ...  \n",
       "2016-12-31 ZH9157 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9159 霾    南风1-2级  重度污染    0.0   450.0    0.0  20.0 -25.0  \n",
       "           ZH9163 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9165 霾    南风1-2级  重度污染    0.0   450.0    0.0  16.0 -20.0  \n",
       "           ZH9168 霾    南风1-2级  重度污染   24.0   480.0    0.0  16.0 -20.0  \n",
       "\n",
       "[200655 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result = pd.merge(left=yucli_plane(), right=yucli_weath(), on=\"日期\", how=\"inner\")\n",
    "df_result.groupby(['日期','航班号','天气状况','风','空气']).sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 3、数据探索，发现数据中的规律"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 4800x3200 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']#用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False#用来正常显示负号\n",
    "\n",
    "figure=plt.figure(figsize=(30,20),dpi=160)\n",
    "\n",
    "df_result1=df_result.groupby('风')['头等舱','公务舱','经济舱'].sum()\n",
    "\n",
    "x_ticks=df_result1.index.values\n",
    "x = np.arange(len(x_ticks))\n",
    "plt.xticks(x, x_ticks)\n",
    "\n",
    "y1=df_result1['头等舱'].values\n",
    "y2=df_result1['公务舱'].values\n",
    "y3=df_result1['经济舱'].values\n",
    "\n",
    "plt.xlabel('风')\n",
    "plt.ylabel('上座率')\n",
    "plt.title('风对上座率的影响')\n",
    "\n",
    "\n",
    "width =0.25\n",
    "plt.bar(x,y1,width=width,color=\"blue\")\n",
    "plt.bar(x+width,y2,width=width,color=\"red\")\n",
    "plt.bar(x+2*width,y3,width=width,color=\"orange\")\n",
    "\n",
    "# for a,b in zip(x,y1):\n",
    "#     plt.text(a,b,format(b,','),ha='center',va='bottom',fontsize=14)\n",
    "# for a,b in zip(x,y2):\n",
    "#     plt.text(a+width,b,format(b,','),ha='center',va='bottom',fontsize=14)\n",
    "# for a,b in zip(x,y3):\n",
    "#     plt.text(a+2*width,b,format(b,','),ha='center',va='bottom',fontsize=14)\n",
    "\n",
    "plt.legend(['头等舱','公务舱','经济舱'])#显示图例\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "figure=plt.figure(figsize=(10,10),dpi=100)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']#用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False#用来正常显示负号\n",
    "\n",
    "\n",
    "df_result2=df_result.groupby('风').sum()\n",
    "# df_result2\n",
    "\n",
    "x_ticks=df_result2.index.values\n",
    "x = np.arange(len(x_ticks))\n",
    "plt.xticks(x, x_ticks)\n",
    "\n",
    "\n",
    "plt.xticks(fontsize=5)\n",
    "plt.yticks(fontsize=8) \n",
    "\n",
    "con1=df_result2['头等舱'].values+df_result2['公务舱'].values+df_result2['经济舱'].values+df_result2['其他'].values\n",
    "con2=df_result2['头等舱总数'].values+df_result2['公务舱总数'].values+df_result2['经济舱总数'].values+df_result2['其他总数'].values\n",
    "\n",
    "\n",
    "\n",
    "width =0.25\n",
    "plt.bar(x,con1,width=width,color=\"orange\")\n",
    "plt.bar(x+width,con2,width=width,color=\"green\")\n",
    "\n",
    "# for a,b in zip(x,con1):\n",
    "#     plt.text(a,b,format(b,','),ha='center',va='bottom',fontsize=5)\n",
    "# for a,b in zip(x,con2):\n",
    "#     plt.text(a+width,b,format(b,','),ha='center',va='bottom',fontsize=5)\n",
    "\n",
    "\n",
    "plt.legend(['其他','其他总数'])#显示图例\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 4、数据分析建模并评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ## 5、整理工程并部署：输入航班号、根据未来一周的天气（网上爬取的天气预报），预测并输出其上座率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "49cb93f377a7abe7414b7b0f21fb3017538004a126cf690fb524202736b7fb92"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
